Literature DB >> 28589538

Lentigo maligna of the face: A quantitative simple method to identify individual patient risk probability on dermoscopy.

Anna Carbone1, Angela Ferrari1, Giovanni Paolino2, Pierluigi Buccini1, Paola De Simone1, Laura Eibenschutz1, Paolo Piemonte1, Vitaliano Silipo1, Isabella Sperduti3, Caterina Catricalà1, Pasquale Frascione1.   

Abstract

BACKGROUND/
OBJECTIVES: The clinical and dermoscopic differential diagnosis of flat pigmented facial lesions represents a great challenge for the clinicians. Our aim was to report a quantitative method based on dermoscopic features to better classify pigmented facial lesions.
METHODS: This is a retrospective case-series study that analysed the dermoscopic features of 582 pigmented facial lesions.
RESULTS: The individual patient probability of lentigo maligna (LM) was predicted by a multivariate model, with an accuracy of 0.72. According to the odds ratio at the multivariate analysis, an individual scoring index was assigned to each criterion, and a value of 4.56 was identified as optimal cut-off point. Up to a score of 2.5, the probability that a lesion is an LM is 0. The probability increases from 10 to 50% for a score ranging between 4.5 and 6. It is about 90% for a score of 7.
CONCLUSION: The optimal cut-off point obtained and the curve that identifies the probability of a patient having a LM could improve the classification and the management strategies of equivocal pigmented facial lesions.
© 2017 The Australasian College of Dermatologists.

Entities:  

Keywords:  dermoscopy; lentigo maligna; lichen planus-like keratosis; pigmented actinic keratosis; pigmented facial lesions; solar lentigo

Mesh:

Year:  2017        PMID: 28589538     DOI: 10.1111/ajd.12595

Source DB:  PubMed          Journal:  Australas J Dermatol        ISSN: 0004-8380            Impact factor:   2.875


  2 in total

1.  Dermatoscopy of flat pigmented facial lesions-evolution of lentigo maligna diagnostic criteria.

Authors:  Miguel Costa-Silva; Ana Calistru; Ana Margarida Barros; Sofia Lopes; Mariana Esteves; Filomena Azevedo
Journal:  Dermatol Pract Concept       Date:  2018-07-31

2.  AK-DL: A Shallow Neural Network Model for Diagnosing Actinic Keratosis with Better Performance Than Deep Neural Networks.

Authors:  Liyang Wang; Angxuan Chen; Yan Zhang; Xiaoya Wang; Yu Zhang; Qun Shen; Yong Xue
Journal:  Diagnostics (Basel)       Date:  2020-04-13
  2 in total

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